Department of Computer Science.
Burnett School of Biomedical Science, College of Medicine, University of Central Orlando, FL 32816, USA.
Bioinformatics. 2020 Jun 1;36(12):3680-3686. doi: 10.1093/bioinformatics/btaa195.
It is a fundamental task to identify microRNAs (miRNAs) targets and accurately locate their target sites. Genome-scale experiments for miRNA target site detection are still costly. The prediction accuracies of existing computational algorithms and tools are often not up to the expectation due to a large number of false positives. One major obstacle to achieve a higher accuracy is the lack of knowledge of the target binding features of miRNAs. The published high-throughput experimental data provide an opportunity to analyze position-wise preference of miRNAs in terms of target binding, which can be an important feature in miRNA target prediction algorithms.
We developed a Markov model to characterize position-wise pairing patterns of miRNA-target interactions. We further integrated this model as a scoring method and developed a dynamic programming (DP) algorithm, MDPS (Markov model-scored Dynamic Programming algorithm for miRNA target site Selection) that can screen putative target sites of miRNA-target binding. The MDPS algorithm thus can take into account both the dependency of neighboring pairing positions and the global pairing information. Based on the trained Markov models from both miRNA-specific and general datasets, we discovered that the position-wise binding information specific to a given miRNA would benefit its target prediction. We also found that miRNAs maintain region-wise similarity in their target binding patterns. Combining MDPS with existing methods significantly improves their precision while only slightly reduces their recall. Therefore, position-wise pairing patterns have the promise to improve target prediction if incorporated into existing software tools.
The source code and tool to calculate MDPS score is available at http://hulab.ucf.edu/research/projects/MDPS/index.html.
Supplementary data are available at Bioinformatics online.
识别 microRNAs (miRNAs) 靶标并准确定位其靶标位点是一项基本任务。miRNA 靶标位点检测的全基因组规模实验仍然昂贵。由于大量的假阳性，现有计算算法和工具的预测准确性往往达不到预期。实现更高准确性的一个主要障碍是缺乏 miRNA 靶标结合特征的知识。已发表的高通量实验数据提供了一个机会，可以根据靶标结合分析 miRNA 位置偏好，这可以成为 miRNA 靶标预测算法中的一个重要特征。
我们开发了一种马尔可夫模型来描述 miRNA-靶相互作用的位置对配模式。我们进一步将该模型集成作为评分方法，并开发了一种动态规划（DP）算法 MDPS（用于 miRNA 靶位选择的马尔可夫模型评分动态规划算法），该算法可以筛选 miRNA-靶结合的假定靶位。因此，MDPS 算法可以同时考虑相邻配对位置的依赖性和全局配对信息。基于来自 miRNA 特异性和通用数据集的训练过的马尔可夫模型，我们发现给定 miRNA 的位置特异性结合信息将有助于其靶标预测。我们还发现 miRNA 在其靶标结合模式中保持区域相似性。将 MDPS 与现有方法结合使用可以显著提高其精度，而仅略微降低召回率。因此，如果将位置对配模式纳入现有的软件工具中，有望提高靶标预测的准确性。
计算 MDPS 得分的源代码和工具可在 http://hulab.ucf.edu/research/projects/MDPS/index.html 上获得。
补充数据可在 Bioinformatics 在线获得。
